Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

100
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
100
Downsampling01:20

Downsampling

197
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
197
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

185
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
185
Deconvolution01:20

Deconvolution

202
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
202
Upsampling01:22

Upsampling

266
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
266
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

436
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
436

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Differences in Reported Anaphylaxis Associated With Common Nonsteroidal Anti-inflammatory Drugs: A Pharmacovigilance Study.

International archives of allergy and immunology·2026
Same author

Microfluidic self-assembled polysaccharide@zein-curcumin ternary nanoparticles: Polysaccharide structure, functional properties and stabilization mechanisms.

Food chemistry·2026
Same author

Functional brain alterations associated with acupuncture for chronic pain: a scoping review of fMRI studies.

Frontiers in neuroscience·2026
Same author

Spatiotemporally programmed nanomedicine engineering to resolve conflicting immunosignals in triple-negative breast cancer.

Signal transduction and targeted therapy·2026
Same author

Dynamic central-peripheral balance in brain-muscle interactions reveals motor impairment in post-stroke hemiplegia: an exploratory study.

Cognitive neurodynamics·2026
Same author

Feature-enhanced dual-input transformer for LIBS quantitative analysis of minor-content elements.

Analytica chimica acta·2026
Same journal

ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion.

IEEE transactions on neural networks and learning systems·2026
Same journal

PIMPC-GNN: Physics-Informed Multiphase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Quantum Rényi α-Entropies for Graph Characterization.

IEEE transactions on neural networks and learning systems·2026
Same journal

LANet: A Lightweight and Accurate Balanced Network Based on State Space Models for Real-Time Semantic Segmentation.

IEEE transactions on neural networks and learning systems·2026
Same journal

MENDNet: Memory-Enhanced Dependency Network for Multistock Movement Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Temporal Mask-Embedding Learning and Query-Refined Head Network for Visual Tracking.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jul 27, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.5K

Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven.

Qiang Zhang, Yaming Zheng, Qiangqiang Yuan

    IEEE Transactions on Neural Networks and Learning Systems
    |June 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This review analyzes noise in hyperspectral images (HSI) and evaluates denoising methods. It provides crucial insights for developing effective HSI denoising algorithms and future research directions.

    More Related Videos

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    00:07

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.1K
    Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging
    09:46

    Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

    Published on: April 28, 2022

    4.0K

    Related Experiment Videos

    Last Updated: Jul 27, 2025

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.5K
    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    00:07

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.1K
    Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging
    09:46

    Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

    Published on: April 28, 2022

    4.0K

    Area of Science:

    • Remote Sensing
    • Image Processing
    • Signal Processing

    Background:

    • Hyperspectral image (HSI) noise significantly impacts data interpretation and application.
    • Effective denoising is crucial for unlocking the full potential of HSI data.

    Purpose of the Study:

    • To provide a comprehensive technical review of Hyperspectral Image (HSI) denoising methods.
    • To analyze noise in various noisy HSIs and identify key considerations for algorithm development.
    • To evaluate and compare existing HSI denoising techniques.

    Main Methods:

    • Noise analysis across different noisy HSI datasets.
    • Formulation of a general HSI restoration model for optimization.
    • Comprehensive review of model-driven, data-driven (including 2-D CNN, 3-D CNN, hybrid, and unsupervised networks), and model-data-driven denoising strategies.
    • Comparative evaluation of denoising methods using simulated and real HSI data.

    Main Results:

    • Summary and contrast of advantages and disadvantages for each denoising strategy.
    • Evaluation of HSI denoising methods based on classification accuracy of denoised images.
    • Assessment of execution efficiency for various HSI denoising techniques.
    • Identification of crucial points for programming HSI denoising algorithms.

    Conclusions:

    • Noise pollution in HSI necessitates advanced denoising techniques for reliable analysis.
    • The review offers a structured overview and comparative analysis of current HSI denoising methods.
    • Future research directions are outlined to advance the field of HSI denoising.